scholarly journals Exact Penalization in Stochastic Programming—Calmness and Constraint Qualification

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Martin Branda
Keyword(s):  
Stochastic Programming ◽  
Error Bound ◽  
Constraint Qualification ◽  
Sufficient Conditions ◽  
Discrete Distributions ◽  
Exact Penalty ◽  
Asymptotic Equivalence ◽  
Exact Penalization ◽  
Local Minimizers ◽  
Constrained Problems

We deal with the conditions which ensure exact penalization in stochastic programming problems under finite discrete distributions. We give several sufficient conditions for problem calmness including graph calmness, existence of an error bound, and generalized Mangasarian-Fromowitz constraint qualification. We propose a new version of the theorem on asymptotic equivalence of local minimizers of chance constrained problems and problems with exact penalty objective. We apply the theory to a problem with a stochastic vanishing constraint.

scholarly journals Error Bound property in mathematical programming

2019 ◽  
Vol 55 (3) ◽  
pp. 309-318
Author(s):  
D. E. Berezhnov ◽  
L. I. Minchenko
Keyword(s):  
Mathematical Programming ◽  
Large Class ◽  
Optimality Conditions ◽  
Error Bound ◽  
Numerical Optimization ◽  
Constraint Qualification ◽  
Necessary Conditions ◽  
Optimization Algorithms ◽  
Sufficient Conditions ◽  
Mathematical Programming Problems

This article is devoted to the Error Bound property (also named R-regularity) in mathematical programming problems. This property plays an important role in analyzing the convergence of numerical optimization algorithms, a topic covered by multiple publications, and at the same time it is a relatively generic constraint qualification that guarantees the satisfaction of the necessary Kuhn – Tucker optimality conditions in mathematical programming problems. In the article, new sufficient conditions for the error bound property are described, and it’s also shown that several known necessary conditions are insufficient. The sufficient conditions obtained can be used to prove the regularity of a large class of sets including sets that cannot be proven regular by other known constraints.

scholarly journals R-Regularity of Set-Valued Mappings Under the Relaxed Constant Positive Linear Dependence Constraint Qualification with Applications to Parametric and Bilevel Optimization

2021 ◽  
Author(s):  
Patrick Mehlitz ◽  
Leonid I. Minchenko
Keyword(s):  
Linear Dependence ◽  
Constraint Qualification ◽  
Parametric Optimization ◽  
Optimization Problems ◽  
Sufficient Conditions ◽  
Bilevel Optimization ◽  
Regularity Property ◽  
Constant Rank Constraint Qualification ◽  
Existence Criterion ◽  
Set Valued Mappings

AbstractThe presence of Lipschitzian properties for solution mappings associated with nonlinear parametric optimization problems is desirable in the context of, e.g., stability analysis or bilevel optimization. An example of such a Lipschitzian property for set-valued mappings, whose graph is the solution set of a system of nonlinear inequalities and equations, is R-regularity. Based on the so-called relaxed constant positive linear dependence constraint qualification, we provide a criterion ensuring the presence of the R-regularity property. In this regard, our analysis generalizes earlier results of that type which exploited the stronger Mangasarian–Fromovitz or constant rank constraint qualification. Afterwards, we apply our findings in order to derive new sufficient conditions which guarantee the presence of R-regularity for solution mappings in parametric optimization. Finally, our results are used to derive an existence criterion for solutions in pessimistic bilevel optimization and a sufficient condition for the presence of the so-called partial calmness property in optimistic bilevel optimization.

scholarly journals CONSTRAINT QUALIFICATIONS IN PARTIAL IDENTIFICATION

2021 ◽  
pp. 1-24
Author(s):  
Hiroaki Kaido ◽  
Francesca Molinari ◽  
Jörg Stoye
Keyword(s):  
Stochastic Programming ◽  
Constraint Qualification ◽  
Constraint Qualifications ◽  
Partial Identification ◽  
Moment Inequalities ◽  
Pure Moment ◽  
High Level

The literature on stochastic programming typically restricts attention to problems that fulfill constraint qualifications. The literature on estimation and inference under partial identification frequently restricts the geometry of identified sets with diverse high-level assumptions. These superficially appear to be different approaches to closely related problems. We extensively analyze their relation. Among other things, we show that for partial identification through pure moment inequalities, numerous assumptions from the literature essentially coincide with the Mangasarian–Fromowitz constraint qualification. This clarifies the relation between well-known contributions, including within econometrics, and elucidates stringency, as well as ease of verification, of some high-level assumptions in seminal papers.

scholarly journals Randomized sketch descent methods for non-separable linearly constrained optimization

2020 ◽  
Author(s):  
Ion Necoara ◽  
Martin Takáč
Keyword(s):  
Objective Function ◽  
Large Scale ◽  
Convergence Rates ◽  
Optimization Problems ◽  
Sufficient Conditions ◽  
Linear Constraints ◽  
Descent Methods ◽  
Constrained Problems ◽  
Special Cases ◽  
Linearly Constrained

Abstract In this paper we consider large-scale smooth optimization problems with multiple linear coupled constraints. Due to the non-separability of the constraints, arbitrary random sketching would not be guaranteed to work. Thus, we first investigate necessary and sufficient conditions for the sketch sampling to have well-defined algorithms. Based on these sampling conditions we develop new sketch descent methods for solving general smooth linearly constrained problems, in particular, random sketch descent (RSD) and accelerated random sketch descent (A-RSD) methods. To our knowledge, this is the first convergence analysis of RSD algorithms for optimization problems with multiple non-separable linear constraints. For the general case, when the objective function is smooth and non-convex, we prove for the non-accelerated variant sublinear rate in expectation for an appropriate optimality measure. In the smooth convex case, we derive for both algorithms, non-accelerated and A-RSD, sublinear convergence rates in the expected values of the objective function. Additionally, if the objective function satisfies a strong convexity type condition, both algorithms converge linearly in expectation. In special cases, where complexity bounds are known for some particular sketching algorithms, such as coordinate descent methods for optimization problems with a single linear coupled constraint, our theory recovers the best known bounds. Finally, we present several numerical examples to illustrate the performances of our new algorithms.

Smoothing Partially Exact Penalty Function of Biconvex Programming

2020 ◽  
Vol 37 (04) ◽  
pp. 2040018
Author(s):  
Rui Shen ◽  
Zhiqing Meng ◽  
Min Jiang
Keyword(s):  
Error Bounds ◽  
Penalty Function ◽  
Constraint Qualification ◽  
Feasible Solution ◽  
Exact Penalty Function ◽  
Exact Penalty ◽  
Kkt Condition ◽  
Optimum Point ◽  
Kkt Point ◽  
Slater Constraint Qualification

In this paper, a smoothing partial exact penalty function of biconvex programming is studied. First, concepts of partial KKT point, partial optimum point, partial KKT condition, partial Slater constraint qualification and partial exactness are defined for biconvex programming. It is proved that the partial KKT point is equal to the partial optimum point under the condition of partial Slater constraint qualification and the penalty function of biconvex programming is partially exact if partial KKT condition holds. We prove the error bounds properties between smoothing penalty function and penalty function of biconvex programming when the partial KKT condition holds, as well as the error bounds between objective value of a partial optimum point of smoothing penalty function problem and its [Formula: see text]-feasible solution. So, a partial optimum point of the smoothing penalty function optimization problem is an approximately partial optimum point of biconvex programming. Second, based on the smoothing penalty function, two algorithms are presented for finding a partial optimum or approximate [Formula: see text]-feasible solution to an inequality constrained biconvex optimization and their convergence is proved under some conditions. Finally, numerical experiments show that a satisfactory approximate solution can be obtained by the proposed algorithm.

An RQP algorithm using a differentiable exact penalty function for inequality constrained problems

1992 ◽  
Vol 55 (1-3) ◽  
pp. 49-68 ◽  
Author(s):  
Gianni Di Pillo ◽  
Francisco Facchinei ◽  
Luigi Grippo
Keyword(s):  
Penalty Function ◽  
Exact Penalty Function ◽  
Exact Penalty ◽  
Constrained Problems

On a problem of Hartman and Wintner

1998 ◽  
Vol 128 (5) ◽  
pp. 1007-1022 ◽  
Author(s):  
N. Chernyavskaya
Keyword(s):  
Sufficient Conditions ◽  
Asymptotic Equivalence ◽  
Sturm Liouville ◽  
Fundamental Systems

The Hartman–Wintner problem on asymptotic equivalence of fundamental systems of solutions (FSSs) for two Sturm–Liouville equations is studied. The following results are obtained: a criterion of asymptotic equivalence of FSSs, and sufficient conditions of asymptotic equivalence of FSSs which are expressed in terms of the coefficients of the considered equations only.

scholarly journals Equilibria of a clamped Euler beam (Elastica) with distributed load: Large deformations

2017 ◽  
Vol 27 (08) ◽  
pp. 1391-1421 ◽  
Author(s):  
Alessandro Della Corte ◽  
Francesco dell’Isola ◽  
Raffaele Esposito ◽  
Mario Pulvirenti
Keyword(s):  
Total Energy ◽  
Large Deformations ◽  
Sufficient Conditions ◽  
Direct Methods ◽  
Local Minimizers ◽  
Dead Load ◽  
Euler Beam ◽  
Distributed Load ◽  
Stability And Instability ◽  
Equilibrium Shapes

We present some novel equilibrium shapes of a clamped Euler beam (Elastica from now on) under uniformly distributed dead load orthogonal to the straight reference configuration. We characterize the properties of the minimizers of total energy, determine the corresponding Euler–Lagrange conditions and prove, by means of direct methods of calculus of variations, the existence of curled local minimizers. Moreover, we prove some sufficient conditions for stability and instability of solutions of the Euler–Lagrange, that can be applied to numerically found curled shapes.

ROBUST SYNCHRONIZATION OF CHAOTIC LUR'E SYSTEMS VIA REPLACING VARIABLES CONTROL

2006 ◽  
Vol 16 (11) ◽  
pp. 3421-3433 ◽  
Author(s):  
XIAOFENG WU ◽  
MUHONG WANG
Keyword(s):  
Frequency Domain ◽  
Error Bound ◽  
Sufficient Conditions ◽  
Synchronization Error ◽  
Linear Matrix ◽  
Lur’E Systems ◽  
Parameter Mismatch ◽  
Chaotic Lur’E Systems ◽  
Definition Of ◽  
Synchronization Scheme

The sufficient conditions for chaos synchronization of two nonidentical systems by replacing variables control have not been proposed until now. In this paper, synchronization of two chaotic Lur'e systems with parameter mismatch by replacing variables control is studied. First of all, we present a master-slave Lur'e systems synchronization scheme with both parameter mismatch and replacing variables control, and derive a responsive error system for the scheme. A new definition of synchronization with finite L 2-gain is then introduced. Based on the definition, the sufficient synchronization criteria which are in the form of linear matrix inequality (LMI) are proved using a quadratic Lyapunov function. By means of MKY lemma the frequency domain criteria are further derived from the obtained LMIs. These frequency domain criteria are illustrated on the master-slave Chua's circuits with parameter mismatch so that the ranges of the parameters of Chua's circuit are analytically solved in the sense of the synchronization with finite L 2-gain by replacing singe-variable control. The illustrative examples verify that within the ranges of the parameters it is possible to synchronize the master-slave Chua's circuits up to a small synchronization error bound, even the qualitative behaviors of the slave circuit are different from that of the master one, such as the trajectory of the master circuit is chaotic and that of the slave divergent. The relation between the synchronization error bound and parameter mismatch is shown.

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